Similar compounds versus similar conformers: complementarity between PubChem 2-D and 3-D neighboring sets

Interpretable AI and Machine Learning Classification for Identifying High-Efficiency Donor-Acceptor Pairs in Organic Solar Cells

To enhance the efficiency of organic solar cells, accurately predicting the efficiency of new pairs of donor and acceptor materials is crucial. Presently, most machine learning studies rely on regression models, which often struggle to establish clear rules for distinguishing between high- and low-performing donor-acceptor pairs. This study proposes a novel approach by integrating interpretable AI, specifically using Shapely values, with four supervised machine learning classification models, namely, support vector machines, decision trees, random forest, and gradient boosting. These models aim to identify high-efficiency donor-acceptor pairs based solely on chemical structures and to extract important features that establish general design principles for distinguishing between high- and low-efficiency pairs. For validation purposes, an unsupervised machine learning algorithm utilizing loading vectors obtained from the principal component analysis is employed to identify crucial features associated with high-efficiency donor-acceptor pairs. Interestingly, the features identified by the supervised machine learning approach were found to be a subset of those identified by the unsupervised method. Noteworthy features include the van der Waals surface area, partial equalization of orbital electronegativity, Moreau-Broto autocorrelation, and molecular substructures. Leveraging these features, a backward-working model can be developed, facilitating exploration across a wide array of materials used in organic solar cells. This innovative approach will help navigate the vast chemical compound space of donor and acceptor materials essential in creating high-efficiency organic solar cells.

Rebamipide and Derivatives are Potent, Selective Inhibitors of Histidine Phosphatase Activity of the Suppressor of T Cell Receptor Signaling Proteins

The suppressor of T cell receptor signaling (Sts) proteins are negative regulators of immune signaling. Genetic inactivation of these proteins leads to significant resistance to infection. From a 590,000 compound high-throughput screen, we identified the 2-(1H)-quinolinone derivative, rebamipide, as a putative inhibitor of Sts phosphatase activity. Rebamipide, and a small library of derivatives, are competitive, selective inhibitors of Sts-1 with IC50 values from low to submicromolar. SAR analysis indicates that the quinolinone, the acid, and the amide moieties are all essential for activity. A crystal structure confirmed the SAR and reveals key interactions between this class of compound and the protein. Although rebamipide has poor cell permeability, we demonstrated that a liposomal preparation can inactivate the phosphatase activity of Sts-1 in cells. These studies demonstrate that Sts-1 enzyme activity can be pharmacologically inactivated and provide foundational tools and insights for the development of immune-enhancing therapies that target the Sts proteins.

Exploring Chemical Information in PubChem

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical database that serves scientific communities as well as the general public. This database collects chemical information from hundreds of data sources and organizes them into multiple data collections, including Substance, Compound, BioAssay, Protein, Gene, Pathway, and Patent. These collections are interlinked with each other, allowing users to discover related records in the various collections (e.g., drugs targeting a protein or genes modulated by a chemical). PubChem can be searched by keyword (e.g., a chemical, protein, or gene name) as well as by chemical structure. The input structure can be provided using popular line notations or drawn with the PubChem Sketcher. PubChem supports various types of structure searches, including identity search, 2‐D and 3‐D similarity searches, and substructure and superstructure searches. Results from multiple searches can be combined using Boolean operators (i.e., AND, OR, and NOT) to formulate complex queries. PubChem allows the user to quickly retrieve a list of records annotated with a particular classification or ontological term. This paper provides step‐by‐step instructions on how to explore PubChem data with examples of commonly requested tasks. © 2021. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC.

Basic Protocol 1: Finding genes and proteins that interact with a given compound

Basic Protocol 2: Finding drug‐like compounds similar to a query compound through a two‐dimensional (2‐D) similarity search

Basic Protocol 3: Finding compounds similar to a query compound through a three‐dimensional (3‐D) similarity search

Support Protocol: Computing similarity scores between compounds

Basic Protocol 4: Getting the bioactivity data for the hit compounds from substructure search

Basic Protocol 5: Finding drugs that target a particular gene

Basic Protocol 6: Getting bioactivity data of all chemicals tested against a protein.

Basic Protocol 7: Finding compounds annotated with classifications or ontological terms

Basic Protocol 8: Finding stereoisomers and isotopomers of a compound through identity search

Identification of high affinity and low molecular alternatives of boceprevir against SARS-CoV-2 main protease: A virtual screening approach

SARS-CoV-2 has posed global challenge for healthcare due to COVID-19. The main protease (M^pro) of this virus is considered as a major target for drug development efforts. In this work, we have used virtual screening approach with molecular dynamics simulations to identify high affinity and low molecular weight alternatives of boceprevir, a repurposed drug currently being evaluated against M^pro. Out of 180 compounds screened, two boceprevir analogs (PubChem ID: 57841991 and 58606278) were reported as potential alternatives with comparable predicted protease inhibitor potential and pharmacological properties. Further experimental validation of the reported compounds may contribute to the ongoing investigation of boceprevir.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

PUG-View: programmatic access to chemical annotations integrated in PubChem

PubChem is a chemical data repository that provides comprehensive information on various chemical entities. It contains a wealth of chemical information from hundreds of data sources. Programmatic access to this large amount of data provides researchers with new opportunities for data-intensive research. PubChem provides several programmatic access routes. One of these is PUG-View, which is a Representational State Transfer (REST)-style web service interface specialized for accessing annotation data contained in PubChem. The present paper describes various aspects of PUG-View, including the scope of data accessible through PUG-View, the syntax for formulating a PUG-View request URL, the difference of PUG-View from other web service interfaces in PubChem, and its limitations and usage policies.

An update on PUG-REST: RESTful interface for programmatic access to PubChem

PubChem (https://pubchem.ncbi.nlm.nih.gov) is one of the largest open chemical information resources available. It currently receives millions of unique users per month on average, serving as a key resource for many research fields such as cheminformatics, chemical biology, medicinal chemistry, and drug discovery. PubChem provides multiple programmatic access routes to its data and services. One of them is PUG-REST, a Representational State Transfer (REST)-like web service interface to PubChem. On average, PUG-REST receives more than a million requests per day from tens of thousands of unique users. The present paper provides an update on PUG-REST since our previous paper published in 2015. This includes access to new kinds of data (e.g. concise bioactivity data, table of contents headings, etc.), full implementation of synchronous fast structure search, support for assay data retrieval using accession identifiers in response to the deprecation of NCBI’s GI numbers, data exchange between PUG-REST and NCBI’s E-Utilities through the List Gateway, implementation of dynamic traffic control through throttling, and enhanced usage policies. In addition, example Perl scripts are provided, which the user can easily modify, run, or translate into another scripting language.

A Computational Bipartite Graph-Based Drug Repurposing Method

We present a bipartite graph-based approach to calculate drug pairwise similarity for identifying potential new indications of approved drugs. Both chemical and molecular features were used in drug similarity calculation. In this paper, we first extracted drug chemical structures and drug-target interactions. Second, we computed chemical structure similarity and drug- target profile similarity. Further, we constructed a bipartite graph model with known relationships between drugs and their target proteins. Finally, we weighted summing drug structure similarity with target profile similarity to derive drug pairwise similarity, so that we can predict potential indication of a drug from its similar drugs. In addition, we summarized some alternative strategies and variations follow-up to each section in the overall analysis.

Computational approaches to chemical hazard assessment

Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models.

Finding Potential Multitarget Ligands Using PubChem

PubChem ( https://pubchem.ncbi.nlm.nih.gov ) is a key chemical information resource, developed and maintained by the US National Institutes of Health. The present chapter describes how to find potential multitarget ligands from PubChem that would be tested in further experiments. While the protocol presented here uses PubChem's Web-based interfaces to allow users to follow it interactively, it can also be implemented in computer software by using programmatic access interfaces to PubChem (such as PUG-REST or E-Utilities).